Optimization of Tug Services in Tanjung Perak Port Using Assignment Model Based on Forecasting Results of Tug Services

Abstract

Optimizing adequate tugboat services is very much needed to support the operational improvement of the Tanjung Perak port. This study uses the triple exponential smoothing method to predict the number of tug service requests in 2021 and the assignment model to determine the optimal level of operating tugboats. The data used in this study is data on demand for tugboat services for small, medium, and large vessels from 2019 to 2020. Forecasting results show that the highest demand for small boat services is 4551 and 3235. The highest demand for medium vessel services is 479 and the lowest is 365. Meanwhile, for the highest demand for large ship services 61 and the lowest 40. The assignment results show the optimization of Tanjung Perak port by operating 13 tugboats every day.

Optimasi Model Penugasan Berdasarkan Peramalan Layanan Kapal Tunda Di Pelabuhan Tanjung Perak Menggunakan Metode Backpropagation

“…Optimalisasi penggunaan kapal tunda dapat diketahui dengan meramalkan jumlah permintaan pelayanan kapal tunda, yang kemudian akan dimasukkan dalam suatu model untuk mengetahui tingkat optimalisasinya [7]. Beberapa penelitian yang melakukan optimalisasi dengan menggunakan metode penugasan antara lain, penelitian yang dilakukan Zeng et al dalam mengoptimalisasi penugasan mobil untuk menekan penyebaran virus covid-19 [8].…”

“…Hasilnya akan menunjukkan rata-rata waktu tunggu antrian, jumlah antrian, dan tingkat kesibukan pada kapal tunda. Skenario dikatakan optimal ketika rata-rata waktu tunggu antrian dan jumlah antrian rendah, sedangkan rata-rata tingkat kesibukan kapal tunda tinggi [7]. Hasil penugasan dengan menerapkan skenario yang telah dibuat dapat dilihat pada Tabel 9 dan Tabel 10.…”

Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms

“…While some current literature has covered PSO-based stock prediction, it is critical to widen the horizons in the stock market with new studies that have used PSO [12]. Recent research findings highlight the effectiveness of Particle Swarm Optimization (PSO) in improving the accuracy of forecasting, specifically demonstrating its superiority over Genetic Algorithms (GA) when applied to optimize seasonal smoothing models [17,18,15]. Furthermore, the bat algorithm is examined as another noteworthy metaheuristic algorithm utilized for tackling intricate optimization difficulties [16,19].…”